Cohesiveness in Financial News and its Relation to Market Volatility

Piškorec, Matija; Antulov-Fantulin, Nino; Novak, Petra Kralj; Mozetič, Igor; Grčar, Miha; Vodenska, Irena; Šmuc, Tomislav

doi:10.1038/srep05038

Cited by 28 publications

(20 citation statements)

References 34 publications

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“…We also perform the Granger causality test [33] to check if the Twitter variables help in the prediction of the price returns. The steps of the procedure applied are summarized as follows [50]:…”

Section: Correlation and Granger Causalitymentioning

confidence: 99%

The Effects of Twitter Sentiment on Stock Price Returns

Ranco

Aleksovski

Caldarelli³

et al. 2015

PLoS ONE

Self Cite

304

191

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Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.

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Section: Correlation and Granger Causalitymentioning

confidence: 99%

The Effects of Twitter Sentiment on Stock Price Returns

Ranco

Aleksovski

Caldarelli³

et al. 2015

PLoS ONE

Self Cite

304

191

View full text Add to dashboard Cite

show abstract

“…Social media and news play an important role in driving the fluctuation of economic indicators and financial markets [1], [2], [3], [4] in a nontrivial fashion. Recently, novel financial markets have emerged, that are exchanging from fiat money (USD, EUR, CNY) to cryptocurrencies and vice versa [5,6].…”

Section: Introductionmentioning

confidence: 99%

Sensing Social Media Signals for Cryptocurrency News

Beck

Huang

Lindner

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

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The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks. * Contributed equally to the first authorship † anino@ethz.ch

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“…Though Bitcoin is the largest of its kind in terms of total market capitalization value, it still suffers from a volatile price. Volatility as a measure of price fluctuations [9], [10] has a significant impact on trade strategies, investment decisions [11] as well as systemic risk [12]. Meanwhile, order book data carrying fined-grained information about price movement and market intentions is proven to be closely related to volatility [2] and influences Bitcoin market with variation over time [6].…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

Guo

Bifet

Antulov-Fantulin

2018

2018 IEEE International Conference on Data Mining (ICDM)

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Bitcoin is one of the most prominent decentralized digital cryptocurrencies, currently having the largest market capitalization among cryptocurrencies. Ability to understand which factors drive the fluctuations of the Bitcoin price and to what extent they are predictable is interesting both from theoretical and practical perspective. In this paper, we study the problem of the Bitcoin short-term volatility forecasting by exploiting volatility history and order book data. Order book, consisting of buy and sell orders over time, reflects the intention of the market and is closely related to the evolution of volatility. We propose temporal mixture models capable of adaptively exploiting both volatility history and order book features for short-term volatility forecasting. By leveraging rolling and incremental learning and evaluation procedures, we demonstrate the prediction performance of our model as well as studying the robustness, in comparison to a variety of statistical and machine learning baselines. Meanwhile, our temporal mixture model enables to decipher time-varying effect of order book features on the volatility. It demonstrates the prospect of our temporal mixture model as an interpretable forecasting framework over heterogeneous Bitcoin data.

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Cohesiveness in Financial News and its Relation to Market Volatility

Cited by 28 publications

References 34 publications

The Effects of Twitter Sentiment on Stock Price Returns

The Effects of Twitter Sentiment on Stock Price Returns

Sensing Social Media Signals for Cryptocurrency News

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

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